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Multiple Sclerosis

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Predicting outcome in clinically isolated syndrome using machine learning.

NeuroImage. Clinical
We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clini...

Transfer learning improves supervised image segmentation across imaging protocols.

IEEE transactions on medical imaging
The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervis...

Multisequence 3-T Image Synthesis from 64-mT Low-Field-Strength MRI Using Generative Adversarial Networks in Multiple Sclerosis.

Radiology
Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; however, these devices produce lower-quality images than conventional high-field-strength scanners. Pur...

Extended Technical and Clinical Validation of Deep Learning-Based Brainstem Segmentation for Application in Neurodegenerative Diseases.

Human brain mapping
Disorders of the central nervous system, including neurodegenerative diseases, frequently affect the brainstem and can present with focal atrophy. This study aimed to (1) optimize deep learning-based brainstem segmentation for a wide range of patholo...

An artificial intelligence model for Lhermitte's sign in patients with pediatric-onset multiple sclerosis: A follow-up study.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University
BACKGROUND: Lhermitte's sign (LS) is an important clinical marker for patients with multiple sclerosis (MS). Research on pediatric-onset MS (POMS) and LS is limited. To date, there has been no research conducted on the clinical and artificial intelli...

Subject-Based Transfer Learning in Longitudinal Multiple Sclerosis Lesion Segmentation.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Accurate and consistent lesion segmentation from magnetic resonance imaging is required for longitudinal multiple sclerosis (MS) data analysis. In this work, we propose two new transfer learning-based pipelines to improve segm...

Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning.

Studies in health technology and informatics
Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover g...

MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the t...

The Effects of Robotic Exoskeleton Gait Training on Improving Walking Adaptability in Persons with MS.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The goal of the present pilot investigation is to examine the effects of 8 weeks of supervised, over-ground gait training using a robotic exoskeleton (RE) compared with a control condition (conventional gait therapy, CGT) in persons with MS with ambu...